Effects of induced water deficit and biofertilization on growth dynamics and bulb yield of onion (<i>Allium cepa</i> L.) in a neotropical semi-arid environment
Bibliographic record
Abstract
In a scenario of world population increase and climate change, an efficient use of water is key for agricultural production. Onion is one of the most profitable crops and can adapt to particular conditions of water stress. The objective of this research was to determine growing degree-days and accumulated radiation under non-stress conditions and yield of an F1 2000 hybrid of onion (Allium cepa L.) under water deficit (WD) and biofertilization in a semi-arid environment. An established nutrient requirement of 247 kg N, 240 kg P2O5 (105 kg P), 240 kg K2O (199 kg K), and two irrigation factors were applied: normal irrigation with a daily and WD with a 3 d interval irrigation frequencies. The effect of biofertilization was evaluated through the inoculation of a microbial consortium (MC) in combination with four NPK fertilizer treatments. The crop accumulated 1334 degree-days and 1188 MJ m−2·d−1 at the time of harvest at 71 d after transplanting. The yield was 36 t·ha−1, similar under both irrigation conditions; and the WD treatment resulted in a 35% water savings, a 47% and 65% increase in water use efficiency and modulus of elasticity, respectively. The MC resulted in a 50% NPK savings under non-limiting water conditions and produced a similar yield compared with the 100% NPK non-inoculated control. The lower irrigation frequency together with the 100% NPK fertilization dose without the MC and the use of the microorganisms and the 50% NPK treatment without water stress are recommended as agrosustainable practices for onion production.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".